There is an image processing technology for finding a target pattern by a verification process for verifying a feature extracted from an image of a verification target with features defined in patterns registered in advance. As an application example of such an image processing technology as just described, character recognition, object detection, biometric authentication and so forth are listed. For example, in biometric authentication, biological information obtained from a living body using various sensors is imaged, and a verification process is performed using feature points extracted from the image obtained by the imaging of the biological information. Various types of authentication technologies have been devised for such biometric authentication, and for example, there are fingerprint authentication utilizing a fingerprint, iris authentication utilizing a thin film texture pattern of the pupil, face authentication utilizing the face, retinal authentication utilizing a blood vessel pattern on the retina, vein authentication utilizing a vein pattern of a palm or a finger and so forth.
In vein authentication that is an example of an image processing technology, from an image (biological image) of a vein pattern picked up by irradiating near infrared light upon a living body part such as a palm of a hand, a finger or the like, feature points that are characteristic points in the image are extracted, and a verification process is performed using the extracted feature points. In the verification process, calculation of a verification score with feature points defined in patterns registered in advance is performed using feature values and so forth calculated from an image in the neighborhood of the feature points. For example, a correlation value of a feature value between feature points on the image and feature points on the registration patterns, a humming distance between bit series of the feature values or the like is utilized as a verification score.
Increase of the number of feature points that are a verification target causes increase the period of time for obtaining a verification result because the number of arithmetic operation targets in the verification process increases. For example, in one-to-many verification (hereinafter, referred to as 1:N verification (N is a natural number equal to or greater than 2)) for verifying a picked up image and a plurality of registration patterns with each other, as the number N of registration patterns that are a verification target increases, increase of the verification time period becomes more conspicuous. Therefore, in a certain conventional technology, an attempt to restrict the verification target among a plurality of feature points is proposed. For example, it is proposed to map, when similarity between two images is evaluated, feature points in an m-dimensional space (m is a natural number equal to or greater than 2) to a one-dimensional space and evaluate the similarity of the images using the distance between the feature points of the two images mapped to the one-dimensional space thereby to reduce the arithmetic operation cost in similarity evaluation. In another conventional technology, in a verification process for specifying data having a coordinate value of the nearest neighbor point whose point-to-point distance to a datum point is smallest, data are sorted in a coordinate value order on a datum coordinate axis set in advance and then data having a coordinate value whose point-to-point distance to the datum point in the datum coordinate axis direction is smaller than a threshold value set in advance are selected, accordingly to restrict the verification target is proposed.